{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import random\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchsummary import summary\n",
    "from torch.utils.data import Dataset, TensorDataset\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = {0: 'Me',\n",
    "           1: 'Others'}\n",
    "#            2:'Me with mask',\n",
    "#            3:'Others with mask'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_images(path):\n",
    "    for filename in os.listdir(path):\n",
    "        if filename.endswith('.jpg'):\n",
    "            filename = path + '/' + filename\n",
    "            image = cv2.imread(filename)\n",
    "            image = cv2.resize(image, (size, size))\n",
    "            image = image.astype('float32') / 255.0 # convert image from (0,255) to (0,1)\n",
    "            image = (image-0.5) / 0.5 # convert image from (0,1) to (-1,1)\n",
    "            image = image.transpose(2,0,1) # conver dimension from (64,64,3) to (3,64,64)\n",
    "            images_raw.append(image)\n",
    "    \n",
    "            # convert the labels into 0 or 1\n",
    "            if path==path_myface:\n",
    "                labels_raw.append(0)\n",
    "            elif path==path_others:\n",
    "                labels_raw.append(1)\n",
    "            elif path==path_myface_masked:\n",
    "                labels_raw.append(0)\n",
    "            else:\n",
    "                labels_raw.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_myface = './data/faces_my'\n",
    "path_others = './data/faces_other'\n",
    "path_myface_masked = './data/faces_my_masked'\n",
    "path_other_masked = './data/faces_other_masked'\n",
    "\n",
    "size = 64\n",
    "images_raw = []\n",
    "labels_raw = []\n",
    "\n",
    "get_images(path_myface)\n",
    "get_images(path_others)\n",
    "get_images(path_myface_masked)\n",
    "get_images(path_other_masked)\n",
    "\n",
    "\n",
    "images_raw = np.array(images_raw)\n",
    "labels_raw = np.array(labels_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set: torch.Size([6400, 3, 64, 64])\n",
      "train labels torch.Size([6400])\n",
      "test set: torch.Size([1600, 3, 64, 64])\n",
      "test labels torch.Size([1600])\n"
     ]
    }
   ],
   "source": [
    "train_X, test_X, train_y, test_y = train_test_split(images_raw, labels_raw, test_size=0.2, random_state=546)\n",
    "train_X = torch.from_numpy(train_X)\n",
    "train_y = torch.from_numpy(train_y)\n",
    "test_X = torch.from_numpy(test_X)\n",
    "test_y = torch.from_numpy(test_y)\n",
    "\n",
    "print('train set:', train_X.shape)\n",
    "print('train labels', train_y.shape)\n",
    "print('test set:', test_X.shape)\n",
    "print('test labels', test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_batch_size = 16\n",
    "test_batch_size = 16\n",
    "\n",
    "trainset = TensorDataset(train_X, train_y)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=train_batch_size, shuffle=True)\n",
    "\n",
    "testset = TensorDataset(test_X, test_y)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=test_batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 3, 64, 64])\n",
      "torch.Size([16])\n",
      "tensor([1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1], dtype=torch.int32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(img):\n",
    "    img = img*0.5 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    \n",
    "    figure(num=None, figsize=(4, 3), dpi=150, edgecolor='k')\n",
    "    plt.axis('off')\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "\n",
    "    \n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "print(images.shape)\n",
    "print(labels.shape)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "\n",
    "        # Convolution 1\n",
    "        # input: 64*64*3\n",
    "        # output 64*64*32\n",
    "        self.cnn1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)\n",
    "        self.batchnorm1 = nn.BatchNorm2d(32)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        \n",
    "        # Avg pool 1\n",
    "        # output: 32*32*32\n",
    "        self.avgpool1 = nn.AvgPool2d(kernel_size=2, stride=2)\n",
    "        # Dropout for regularization\n",
    "        self.dropout = nn.Dropout(p=0.5)\n",
    "        \n",
    "        # Convolution 2\n",
    "        # output: 32*32*64\n",
    "        self.cnn2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
    "        self.batchnorm2 = nn.BatchNorm2d(64)\n",
    "        self.relu2 = nn.ReLU()\n",
    "        \n",
    "        # Avg pool 2\n",
    "        # output: 16*16*64\n",
    "        self.avgpool2 = nn.AvgPool2d(kernel_size=2, stride=2)\n",
    "        self.dropout = nn.Dropout(p=0.5)\n",
    "        \n",
    "        # Convolution 3\n",
    "        # output: 16*16*64\n",
    "        self.cnn3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
    "        self.batchnorm3 = nn.BatchNorm2d(64)\n",
    "        self.relu3 = nn.ReLU()\n",
    "\n",
    "        # Avg pool 3\n",
    "        # output: 8*8*64\n",
    "        self.avgpool3 = nn.AvgPool2d(kernel_size=2, stride=2)\n",
    "        self.dropout = nn.Dropout(p=0.5)\n",
    "        \n",
    "        # Fully Connected 1\n",
    "        self.fc1 = nn.Linear(8*8*64, 512)\n",
    "        self.batchnorm4 = nn.BatchNorm1d(512)\n",
    "        self.relu4 = nn.ReLU()\n",
    "        \n",
    "        # Fully Connected 2\n",
    "        self.fc2 = nn.Linear(512, 2)\n",
    "        self.sigmoid = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        #Convolution 1\n",
    "        out = self.cnn1(x)\n",
    "        out = self.batchnorm1(out)\n",
    "        out = self.relu1(out)\n",
    "        \n",
    "        #Avg pool 1\n",
    "        out = self.avgpool1(out)\n",
    "        \n",
    "        #Convolution 2\n",
    "        out = self.cnn2(out)\n",
    "        out = self.batchnorm2(out)\n",
    "        out = self.relu2(out)\n",
    "        \n",
    "        #Avg pool 2\n",
    "        out = self.avgpool2(out)\n",
    "\n",
    "        #Convolution 3\n",
    "        out = self.cnn3(out)\n",
    "        out = self.batchnorm3(out)\n",
    "        out = self.relu3(out)\n",
    "        \n",
    "        #Avg pool 3\n",
    "        out = self.avgpool3(out)\n",
    "        \n",
    "        #Resize\n",
    "        out = out.view(out.size(0), -1)\n",
    "        \n",
    "        #Dropout\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        #Fully connected 1\n",
    "        out = self.fc1(out)\n",
    "        out = self.batchnorm4(out)\n",
    "        out = self.relu4(out)\n",
    "\n",
    "        #Fully connected 2\n",
    "        out = self.fc2(out)\n",
    "        out = self.sigmoid(out)\n",
    "        return out\n",
    "    \n",
    "net = Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda\n",
      "Device name: GeForce RTX 2070\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 32, 64, 64]             896\n",
      "       BatchNorm2d-2           [-1, 32, 64, 64]              64\n",
      "              ReLU-3           [-1, 32, 64, 64]               0\n",
      "         AvgPool2d-4           [-1, 32, 32, 32]               0\n",
      "            Conv2d-5           [-1, 64, 32, 32]          18,496\n",
      "       BatchNorm2d-6           [-1, 64, 32, 32]             128\n",
      "              ReLU-7           [-1, 64, 32, 32]               0\n",
      "         AvgPool2d-8           [-1, 64, 16, 16]               0\n",
      "            Conv2d-9           [-1, 64, 16, 16]          36,928\n",
      "      BatchNorm2d-10           [-1, 64, 16, 16]             128\n",
      "             ReLU-11           [-1, 64, 16, 16]               0\n",
      "        AvgPool2d-12             [-1, 64, 8, 8]               0\n",
      "          Dropout-13                 [-1, 4096]               0\n",
      "           Linear-14                  [-1, 512]       2,097,664\n",
      "      BatchNorm1d-15                  [-1, 512]           1,024\n",
      "             ReLU-16                  [-1, 512]               0\n",
      "           Linear-17                    [-1, 2]           1,026\n",
      "          Softmax-18                    [-1, 2]               0\n",
      "================================================================\n",
      "Total params: 2,156,354\n",
      "Trainable params: 2,156,354\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.05\n",
      "Forward/backward pass size (MB): 5.32\n",
      "Params size (MB): 8.23\n",
      "Estimated Total Size (MB): 13.60\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Device:\", device)\n",
    "print(\"Device name:\", torch.cuda.get_device_name(0))\n",
    "\n",
    "model = Net().to(device)\n",
    "summary(model, (3,64,64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.to(device)\n",
    "\n",
    "writer = SummaryWriter()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0\n",
      "epoch: 1\n",
      "epoch: 2\n",
      "epoch: 3\n",
      "epoch: 4\n",
      "epoch: 5\n",
      "epoch: 6\n",
      "epoch: 7\n",
      "epoch: 8\n",
      "epoch: 9\n",
      "epoch: 10\n",
      "epoch: 11\n",
      "epoch: 12\n",
      "epoch: 13\n",
      "epoch: 14\n",
      "epoch: 15\n",
      "epoch: 16\n",
      "epoch: 17\n",
      "epoch: 18\n",
      "epoch: 19\n",
      "epoch: 20\n",
      "epoch: 21\n",
      "epoch: 22\n",
      "epoch: 23\n",
      "epoch: 24\n",
      "epoch: 25\n",
      "epoch: 26\n",
      "epoch: 27\n",
      "epoch: 28\n",
      "epoch: 29\n",
      "epoch: 30\n",
      "epoch: 31\n",
      "epoch: 32\n",
      "epoch: 33\n",
      "epoch: 34\n",
      "epoch: 35\n",
      "epoch: 36\n",
      "epoch: 37\n",
      "epoch: 38\n",
      "epoch: 39\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "overall_step = 0\n",
    "epochs = 40\n",
    "\n",
    "for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "    print('epoch:', epoch)\n",
    "    running_loss = 0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs\n",
    "#         inputs, labels = data\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "\n",
    "        # Make predictions, calculate accuracy and update your weights once\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "#         loss = criterion(outputs, labels)\n",
    "        loss = criterion(outputs, labels.long())\n",
    "#         loss = criterion(outputs, torch.max(labels, 1)[1])\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        _, prediction = torch.max(outputs,1)\n",
    "        accuracy = (labels == prediction.squeeze()).float().mean()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % 100 == 99:    # print every 100 mini-batches\n",
    "#             print('Epoch: %d, Batch: %5d, loss: %.3f' %(epoch + 1, i + 1, running_loss / 200))\n",
    "            running_loss = 0.0\n",
    "            \n",
    "            #Any thing that is added to the \"info\" gets plotted in tensorboard\n",
    "            info = {'loss' : loss.item(), 'accuracy': accuracy.item()}      \n",
    "            for tag, value in info.items():\n",
    "                writer.add_scalar(tag, value, overall_step)\n",
    "                writer.flush()\n",
    "\n",
    "            overall_step+=1\n",
    "\n",
    "writer.close()\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_acc_all(): \n",
    "    correct = 0\n",
    "    total = 0\n",
    "#     net.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for data in testloader:\n",
    "            inputs, labels = data\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            outputs = net(inputs)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            \n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "    print('Accuracy of the network on the test images: %f %%' % (100 * correct / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the test images: 99.812500 %\n"
     ]
    }
   ],
   "source": [
    "get_acc_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_acc_class():\n",
    "    class_correct = list(0. for i in range(2))\n",
    "    class_total = list(0. for i in range(2))\n",
    "#     net.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for data in testloader:\n",
    "            inputs, labels = data\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            outputs = net(inputs)\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            c = (predicted == labels).squeeze()\n",
    "            \n",
    "            for i in range(4):\n",
    "                label = labels[i]\n",
    "                class_correct[label] += c[i].item()\n",
    "                class_total[label] += 1\n",
    "\n",
    "    for i in range(2):\n",
    "        print('Accuracy of %5s : %f %%' % (classes[i], 100 * class_correct[i] / class_total[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Yangwei : 99.528302 %\n",
      "Accuracy of Others : 99.468085 %\n"
     ]
    }
   ],
   "source": [
    "get_acc_class()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_model = './model'\n",
    "if not os.path.exists(path_model):\n",
    "    os.makedirs(path_model)\n",
    "\n",
    "torch.save(net.state_dict(), './model/model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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